In typical query submission environments, components such as analytical engines commonly maintain profiles of entities and relevant internal facts (such as, for example, a geographic location associated with a certain internet protocol (IP) address). However, fetching data in response to queries in such environments has become increasingly challenging due to the ever-increasing volume of data and a multitude of unique deployment aspects of systems implemented within such environments.
Existing approaches include using database (DB) tuning processes which cause a debate every time a new fact is added to the system. However, such approaches require re-tuning for every database type, and troubleshooting operations can become challenging as a developer may not know what query was actually run against a DB. Accordingly, a need exists for techniques to efficiently re-write user queries so as to render the queries targeted without limiting the amount of related (or potentially related) information returned upon execution of the queries.